ABSTRACT In view of characteristics of the ship in the optical remote-sensing image, such as multiple dimensions, majority of small objects, crowded arrangement and complex background, and so on, the paper presents a ship detection framework combining the network-fusing multi-level features crossing levels, the rotation region proposal network and the bidirectional recurrent neural network fusing self-attention mechanism. Firstly, we set up a network fusing multi-level features crossing levels because of the multiple scales and diverse characteristics of the remote-sensing ships to increase the precision of feature extraction of the ship, thus improving the performance in the multiple scales, small objects, and complex background problems. Secondly, we separately design the ROI Pooling Layer and the bidirectional recurrent neural network fusing self-attention mechanism, which infuses the prior information of ship dimension and position to realize a good performance and precise ship positioning in crowded scenes. Finally, we verify the effectiveness of the proposed method through experiments, the experimental dataset includes the private dataset designed by us based on Google Earth, the ship dataset in DOTA-Ship and HRSC2016 public ship dataset. The results verify the contributions of each proposed module, and the comparison results show that our proposed method has a state-of-the-art performance.
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